Handheld electronic device and method for disambiguation of text input providing suppression of low probability artificial variants

ABSTRACT

A handheld electronic device includes a reduced QWERTY keyboard and is enabled with disambiguation software that is operable to disambiguate text input. In addition to identifying and outputting representations of language objects that are stored in the memory and that correspond with a text input, the device is able to generate artificial variants in certain circumstances. Each artificial variant is compared with N-gram data on the handheld electronic device and is suppressed from being output if the artificial variant is determined to have a low probability of being the input intended by a user.

BACKGROUND

1. Field

The disclosed and claimed concept relates generally to handheldelectronic devices and, more particularly, to a handheld electronicdevice having a reduced keyboard and a compound text inputdisambiguation function, and also relates to an associated method.

2. Background Information

Numerous types of handheld electronic devices are known. Examples ofsuch handheld electronic devices include, for instance, personal dataassistants (PDAs), handheld computers, two-way pagers, cellulartelephones, and the like. Many handheld electronic devices also featurewireless communication capability, although many such handheldelectronic devices are stand-alone devices that are functional withoutcommunication with other devices.

Such handheld electronic devices are generally intended to be portable,and thus are of a relatively compact configuration in which keys andother input structures often perform multiple functions under certaincircumstances or may otherwise have multiple aspects or featuresassigned thereto. With advances in technology, handheld electronicdevices are built to have progressively smaller form factors yet haveprogressively greater numbers of applications and features residentthereon. As a practical matter, the keys of a keypad can only be reducedto a certain small size before the keys become relatively unusable. Inorder to enable text entry, however, a keypad must be capable ofentering all twenty-six letters of the Latin alphabet, for instance, aswell as appropriate punctuation and other symbols.

One way of providing numerous letters in a small space has been toprovide a “reduced keyboard” in which multiple letters, symbols, and/ordigits, and the like, are assigned to any given key. For example, atouch-tone telephone includes a reduced keypad by providing twelve keys,of which ten have digits thereon, and of these ten keys eight have Latinletters assigned thereto. For instance, one of the keys includes thedigit “2” as well as the letters “A”, “B”, and “C”. Other known reducedkeyboards have included other arrangements of keys, letters, symbols,digits, and the like. Since a single actuation of such a key potentiallycould be intended by the user to refer to any of the letters “A”, “B”,and “C”, and potentially could also be intended to refer to the digit“2”, the input generally is an ambiguous input and is in need of sometype of disambiguation in order to be useful for text entry purposes.

In order to enable a user to make use of the multiple letters, digits,and the like on any given key, numerous keystroke interpretation systemshave been provided. For instance, a “multi-tap” system allows a user tosubstantially unambiguously specify a particular character on a key bypressing the same key a number of times equivalent to the position ofthe desired character on the key. For example, on the aforementionedtelephone key that includes the letters “ABC”, and the user desires tospecify the letter “C”, the user will press the key three times. Whilesuch multi-tap systems have been generally effective for their intendedpurposes, they nevertheless can require a relatively large number of keyinputs compared with the number of characters that ultimately areoutput.

Another exemplary keystroke interpretation system would include keychording, of which various types exist. For instance, a particularcharacter can be entered by pressing two keys in succession or bypressing and holding first key while pressing a second key. Stillanother exemplary keystroke interpretation system would be a“press-and-hold/press-and-release” interpretation function in which agiven key provides a first result if the key is pressed and immediatelyreleased, and provides a second result if the key is pressed and heldfor a short period of time. While they systems have likewise beengenerally effective for their intended purposes, such systems also havetheir own unique drawbacks.

Another keystroke interpretation system that has been employed is asoftware-based text disambiguation function. In such a system, a usertypically presses keys to which one or more characters have beenassigned, generally pressing each key one time for each desired letter,and the disambiguation software attempt to predict the intended input.Numerous such systems have been proposed, and while many have beengenerally effective for their intended purposes, shortcomings stillexist.

It would be desirable to provide an improved handheld electronic devicewith a reduced keyboard that seeks to mimic a QWERTY keyboard experienceor other particular keyboard experience. Such an improved handheldelectronic device might also desirably be configured with enoughfeatures to enable text entry and other tasks with relative ease.

BRIEF DESCRIPTION OF THE DRAWINGS

A full understanding of the disclosed and claimed concept can be gainedfrom the following Description when read in conjunction with theaccompanying drawings in which:

FIG. 1 is a top plan view of an improved handheld electronic device inaccordance with the disclosed and claimed concept;

FIG. 2 is a schematic depiction of the improved handheld electronicdevice of FIG. 1;

FIG. 2A is a schematic depiction of a portion of the handheld electronicdevice of FIG. 2;

FIGS. 3A and 3B are an exemplary flowchart depicting certain aspects ofa disambiguation function that can be executed on the handheldelectronic device of FIG. 1;

FIG. 4 is another exemplary flowchart depicting certain aspects of adisambiguation function that can be executed on the handheld electronicdevice by which certain output variants can be provided to the user;

FIGS. 5A and 5B are another exemplary flowchart depicting certainaspects of a learning method that can be executed on the handheldelectronic device;

FIG. 6 is another exemplary flowchart depicting certain aspects of amethod by which various display formats can be provided on the handheldelectronic device;

FIG. 7 is an exemplary output during a text entry operation;

FIG. 8 is another exemplary output during another part of the text entryoperation;

FIG. 9 is another exemplary output during another part of the text entryoperation;

FIG. 10 is another exemplary output during another part of the textentry operation;

FIG. 11 is an exemplary output on the handheld electronic device duringanother text entry operation;

FIG. 12 is an exemplary output that can be provided in an instance whenthe disambiguation function of the handheld electronic device has beendisabled;

FIG. 13 is an exemplary depiction of a map file stored on the handheldelectronic device;

FIG. 14 is an exemplary depiction of an alphabet stored on the handheldelectronic device;

FIG. 15A is an exemplary output during another text entry operation;

FIG. 15B is another exemplary output during another part of the anothertext entry operation;

FIG. 15C is another exemplary output during another part of the anothertext entry operation;

FIG. 16 is an exemplary depiction of an alphabet stored on the handheldelectronic device; and

FIG. 17 is an exemplary output during another text entry operation.

Similar numerals refer to similar parts throughout the specification.

DESCRIPTION

An improved handheld electronic device 4 is indicated generally in FIG.1 and is depicted schematically in FIG. 2. The exemplary handheldelectronic device 4 includes a housing 6 upon which are disposed aprocessor unit that includes an input apparatus 8, an output apparatus12, a processor 16, a memory 20, and at least a first routine. Theprocessor 16 may be, for instance, and without limitation, amicroprocessor (μP) and is responsive to inputs from the input apparatus8 and provides output signals to the output apparatus 12. The processor16 also interfaces with the memory 20. The processor 16 and the memory20 together form a processor apparatus. Examples of handheld electronicdevices are included in U.S. Pat. Nos. 6,452,588 and 6,489,950, whichare incorporated by record herein.

As can be understood from FIG. 1, the input apparatus 8 includes akeypad 24 and a thumbwheel 32. As will be described in greater detailbelow, the keypad 24 is in the exemplary form of a reduced QWERTYkeyboard including a plurality of keys 28 that serve as input members.It is noted, however, that the keypad 24 may be of other configurations,such as an AZERTY keyboard, a QWERTZ keyboard, or other keyboardarrangement, whether presently known or unknown, and either reduced ornot reduced. As employed herein, the expression “reduced” and variationsthereof in the context of a keyboard, a keypad, or other arrangement ofinput members, shall refer broadly to an arrangement in which at leastone of the input members has assigned thereto a plurality of linguisticelements such as, for example, characters in the set of Latin letters,whereby an actuation of the at least one of the input members, withoutanother input in combination therewith, is an ambiguous input since itcould refer to more than one of the plurality of linguistic elementsassigned thereto. As employed herein, the expression “linguisticelement” and variations thereof shall refer broadly to any element thatitself can be a language object or from which a language object can beconstructed, identified, or otherwise obtained, and thus would include,for example and without limitation, characters, letters, strokes,ideograms, phonemes, morphemes, digits, and the like. As employedherein, the expression “language object” and variations thereof shallrefer broadly to any type of object that may be constructed, identified,or otherwise obtained from one or more linguistic elements, that can beused alone or in combination to generate text, and that would include,for example and without limitation, words, shortcuts, symbols,ideograms, and the like.

The system architecture of the handheld electronic device 4advantageously is organized to be operable independent of the specificlayout of the keypad 24. Accordingly, the system architecture of thehandheld electronic device 4 can be employed in conjunction withvirtually any keypad layout substantially without requiring anymeaningful change in the system architecture. It is further noted thatcertain of the features set forth herein are usable on either or both ofa reduced keyboard and a non-reduced keyboard.

The keys 28 are disposed on a front face of the housing 6, and thethumbwheel 32 is disposed at a side of the housing 6. The thumbwheel 32can serve as another input member and is both rotatable, as is indicatedby the arrow 34, to provide selection inputs to the processor 16, andalso can be pressed in a direction generally toward the housing 6, as isindicated by the arrow 38, to provide another selection input to theprocessor 16.

Among the keys 28 of the keypad 24 are a <NEXT> key 40 and an <ENTER>key 44. The <NEXT> key 40 can be pressed to provide a selection input tothe processor 16 and provides substantially the same selection input asis provided by a rotational input of the thumbwheel 32. Since the <NEXT>key 40 is provided adjacent a number of the other keys 28 of the keypad24, the user can provide a selection input to the processor 16substantially without moving the user's hands away from the keypad 24during a text entry operation. As will be described in greater detailbelow, the <NEXT> key 40 additionally and advantageously includes agraphic 42 disposed thereon, and in certain circumstances the outputapparatus 12 also displays a displayed graphic 46 thereon to identifythe <NEXT> key 40 as being able to provide a selection input to theprocessor 16. In this regard, the displayed graphic 46 of the outputapparatus 12 is substantially similar to the graphic 42 on the <NEXT>key and thus identifies the <NEXT> key 40 as being capable of providinga desirable selection input to the processor 16.

As can further be seen in FIG. 1, many of the keys 28 include a numberof linguistic elements 48 disposed thereon. As employed herein, theexpression “a number of” and variations thereof shall refer broadly toany quantity, including a quantity of one, and in certain circumstancesherein can also refer to a quantity of zero. In the exemplary depictionof the keypad 24, many of the keys 28 include two linguistic elements,such as including a first linguistic element 52 and a second linguisticelement 56 assigned thereto.

One of the keys 28 of the keypad 24 includes as the characters 48thereof the letters “Q” and “W”, and an adjacent key 28 includes as thecharacters 48 thereof the letters “E” and “R”. It can be seen that thearrangement of the characters 48 on the keys 28 of the keypad 24 isgenerally of a QWERTY arrangement, albeit with many of the keys 28including two of the characters 48.

The output apparatus 12 includes a display 60 upon which can be providedan output 64. An exemplary output 64 is depicted on the display 60 inFIG. 1. The output 64 includes a text component 68 and a variantcomponent 72. The variant component 72 includes a default portion 76 anda variant portion 80. The display also includes a caret 84 that depictsgenerally where the next input from the input apparatus 8 will bereceived.

The text component 68 of the output 64 provides a depiction of thedefault portion 76 of the output 64 at a location on the display 60where the text is being input. The variant component 72 is disposedgenerally in the vicinity of the text component 68 and provides, inaddition to the default proposed output 76, a depiction of the variousalternate text choices, i.e., alternates to the default proposed output76, that are proposed by an input disambiguation function in response toan input sequence of key actuations of the keys 28.

As will be described in greater detail below, the default portion 76 isproposed by the disambiguation function as being the most likelydisambiguated interpretation of the ambiguous input provided by theuser. The variant portion 80 includes a predetermined quantity ofalternate proposed interpretations of the same ambiguous input fromwhich the user can select, if desired. The displayed graphic 46typically is provided in the variant component 72 in the vicinity of thevariant portion 80, although it is understood that the displayed graphic46 could be provided in other locations and in other fashions. It isalso noted that the exemplary variant portion 80 is depicted herein asextending vertically below the default portion 76, but it is understoodthat numerous other arrangements could be provided.

Among the keys 28 of the keypad 24 additionally is a <DELETE> key 86that can be provided to delete a text entry. As will be described ingreater detail below, the <DELETE> key 86 can also be employed inproviding an alternation input to the processor 16 for use by thedisambiguation function.

The memory 20 is depicted schematically in FIG. 2A. The memory 20 can beany of a variety of types of internal and/or external storage media suchas, without limitation, RAM, ROM, EPROM(s), EEPROM(s), and the like thatprovide a storage register for data storage such as in the fashion of aninternal storage area of a computer, and can be volatile memory ornonvolatile memory. The memory 20 additionally includes a number ofroutines depicted generally with the numeral 22 for the processing ofdata. The routines 22 can be in any of a variety of forms such as,without limitation, software, firmware, and the like. As will beexplained in greater detail below, the routines 22 include theaforementioned disambiguation function as an application, as well asother routines.

As can be understood from FIG. 2A, the memory 20 additionally includesdata stored and/or organized in a number of tables, sets, lists, and/orotherwise. Specifically, the memory 20 includes a generic word list 88,a new words database 92, and a frequency learning database 96. Thememory 20 additionally has stored therein another data source 99 and amap file 49, both of which are described elsewhere herein in greaterdetail.

Stored within the various areas of the memory 20 are a number oflanguage objects 100 and frequency objects 104. The language objects 100generally are each associated with an associated frequency object 104.The language objects 100 include, in the present exemplary embodiment, aplurality of word objects 108 and a plurality of N-gram objects 112. Theword objects 108 are generally representative of complete words withinthe language or custom words stored in the memory 20. For instance, ifthe language stored in the memory 20 is, for example, English, generallyeach word object 108 would represent a word in the English language orwould represent a custom word.

Associated with substantially each word object 108 is a frequency object104 having frequency value that is indicative of the relative frequencywithin the relevant language of the given word represented by the wordobject 108. In this regard, the generic word list 88 includes a corpusof word objects 108 and associated frequency objects 104 that togetherare representative of a wide variety of words and their relativefrequency within a given vernacular of, for instance, a given language.The generic word list 88 can be derived in any of a wide variety offashions, such as by analyzing numerous texts and other language sourcesto determine the various words within the language sources as well astheir relative probabilities, i.e., relative frequencies, of occurrencesof the various words within the language sources.

The N-gram objects 112 stored within the generic word list 88 are shortstrings of characters within the relevant language typically, forexample, one to three characters in length, and typically represent wordfragments within the relevant language, although certain of the N-gramobjects 112 additionally can themselves be words. However, to the extentthat an N-gram object 112 also is a word within the relevant language,the same word likely would be separately stored as a word object 108within the generic word list 88. As employed herein, the expression“string” and variations thereof shall refer broadly to an object havingone or more characters or components, and can refer to any of a completeword, a fragment of a word, a custom word or expression, and the like.

In the present exemplary embodiment of the handheld electronic device 4,the N-gram objects 112 include 1-gram objects, i.e., string objects thatare one character in length, 2-gram objects, i.e., string objects thatare two characters in length, and 3-gram objects, i.e., string objectsthat are three characters in length, all of which are collectivelyreferred to as N-grams 112. Substantially each N-gram object 112 in thegeneric word list 88 is similarly associated with an associatedfrequency object 104 stored within the generic word list 88, but thefrequency object 104 associated with a given N-gram object 112 has afrequency value that indicates the relative probability that thecharacter string represented by the particular N-gram object 112 existsat any location within any word of the relevant language. The N-gramobjects 112 and the associated frequency objects 104 are a part of thecorpus of the generic word list 88 and are obtained in a fashion similarto the way in which the word object 108 and the associated frequencyobjects 104 are obtained, although the analysis performed in obtainingthe N-gram objects 112 will be slightly different because it willinvolve analysis of the various character strings within the variouswords instead of relying primarily on the relative occurrence of a givenword.

The present exemplary embodiment of the handheld electronic device 4,with its exemplary language being the English language, includestwenty-six 1-gram N-gram objects 112, i.e., one 1-gram object for eachof the twenty-six letters in the Latin alphabet upon which the Englishlanguage is based, and further includes 676 2-gram N-gram objects 112,i.e., twenty-six squared, representing each two-letter permutation ofthe twenty-six letters within the Latin alphabet.

The N-gram objects 112 also include a certain quantity of 3-gram N-gramobjects 112, primarily those that have a relatively high frequencywithin the relevant language. The exemplary embodiment of the handheldelectronic device 4 includes fewer than all of the three-letterpermutations of the twenty-six letters of the Latin alphabet due toconsiderations of data storage size, and also because the 2-gram N-gramobjects 112 can already provide a meaningful amount of informationregarding the relevant language. As will be set forth in greater detailbelow, the N-gram objects 112 and their associated frequency objects 104provide frequency data that can be attributed to character strings forwhich a corresponding word object 108 cannot be identified or has notbeen identified, and typically is employed as a fallback data source,although this need not be exclusively the case.

In the present exemplary embodiment, the language objects 100 and thefrequency objects 104 are maintained substantially inviolate in thegeneric word list 88, meaning that the basic language corpus remainssubstantially unaltered within the generic word list 88, and thelearning functions that are provided by the handheld electronic device 4and that are described below operate in conjunction with other objectthat are generally stored elsewhere in memory 20, such as, for example,in the new words database 92 and the frequency learning database 96.

The new words database 92 and the frequency learning database 96 storeadditional word objects 108 and associated frequency objects 104 inorder to provide to a user a customized experience in which words andthe like that are used relatively more frequently by a user will beassociated with relatively higher frequency values than might otherwisebe reflected in the generic word list 88. More particularly, the newwords database 92 includes word objects 108 that are user-defined andthat generally are not found among the word objects 108 of the genericword list 88. Each word object 108 in the new words database 92 hasassociated therewith an associated frequency object 104 that is alsostored in the new words database 92. The frequency learning database 96stores word objects 108 and associated frequency objects 104 that areindicative of relatively more frequent usage of such words by a userthan would be reflected in the generic word list 88. As such, the newwords database 92 and the frequency learning database 96 provide twolearning functions, that is, they together provide the ability to learnnew words as well the ability to learn altered frequency values forknown words.

FIGS. 3A and 3B depicts in an exemplary fashion the general operation ofcertain aspects of the disambiguation function of the handheldelectronic device 4. Additional features, functions, and the like aredepicted and described elsewhere.

An input is detected, as at 204, and the input can be any type ofactuation or other operation as to any portion of the input apparatus 8.A typical input would include, for instance, an actuation of a key 28having a number of characters 48 thereon, or any other type of actuationor manipulation of the input apparatus 8.

Upon detection at 204 of an input, a timer is reset at 208. The use ofthe timer will be described in greater detail below.

The disambiguation function then determines, as at 212, whether thecurrent input is an operational input, such as a selection input, adelimiter input, a movement input, an alternation input, or, forinstance, any other input that does not constitute an actuation of a key28 having a number of characters 48 thereon. If the input is determinedat 212 to not be an operational input, processing continues at 216 byadding the input to the current input sequence which may or may notalready include an input.

Many of the inputs detected at 204 are employed in generating inputsequences as to which the disambiguation function will be executed. Aninput sequence is build up in each “session” with each actuation of akey 28 having a number of characters 48 thereon. Since an input sequencetypically will be made up of at least one actuation of a key 28 having aplurality of characters 48 thereon, the input sequence will beambiguous. When a word, for example, is completed the current session isended an a new session is initiated.

An input sequence is gradually built up on the handheld electronicdevice 4 with each successive actuation of a key 28 during any givensession. Specifically, once a delimiter input is detected during anygiven session, the session is terminated and a new session is initiated.Each input resulting from an actuation of one of the keys 28 having anumber of the characters 48 associated therewith is sequentially addedto the current input sequence. As the input sequence grows during agiven session, the disambiguation function generally is executed witheach actuation of a key 28, i.e., and input, and as to the entire inputsequence. Stated otherwise, within a given session, the growing inputsequence is attempted to be disambiguated as a unit by thedisambiguation function with each successive actuation of the variouskeys 28.

Once a current input representing a most recent actuation of the one ofthe keys 28 having a number of the characters 48 assigned thereto hasbeen added to the current input sequence within the current session, asat 216 in FIG. 3A, the disambiguation function generates, as at 220,substantially all of the permutations of the characters 48 assigned tothe various keys 28 that were actuated in generating the input sequence.In this regard, the “permutations” refer to the various strings that canresult from the characters 48 of each actuated key 28 limited by theorder in which the keys 28 were actuated. The various permutations ofthe characters in the input sequence are employed as prefix objects.

For instance, if the current input sequence within the current sessionis the ambiguous input of the keys “AS” and “OP”, the variouspermutations of the first character 52 and the second character 56 ofeach of the two keys 28, when considered in the sequence in which thekeys 28 were actuated, would be “SO”, “SP”, “AP”, and “AO”, and each ofthese is a prefix object that is generated, as at 220, with respect tothe current input sequence. As will be explained in greater detailbelow, the disambiguation function seeks to identify for each prefixobject one of the word objects 108 for which the prefix object would bea prefix.

For each generated prefix object, the memory 20 is consulted, as at 224,to identify, if possible, for each prefix object one of the word objects108 in the memory 20 that corresponds with the prefix object, meaningthat the sequence of letters represented by the prefix object would beeither a prefix of the identified word object 108 or would besubstantially identical to the entirety of the word object 108. Furtherin this regard, the word object 108 that is sought to be identified isthe highest frequency word object 108. That is, the disambiguationfunction seeks to identify the word object 108 that corresponds with theprefix object and that also is associated with a frequency object 104having a relatively higher frequency value than any of the otherfrequency objects 104 associated with the other word objects 108 thatcorrespond with the prefix object.

It is noted in this regard that the word objects 108 in the generic wordlist 88 are generally organized in data tables that correspond with thefirst two letters of various words. For instance, the data tableassociated with the prefix “CO” would include all of the words such as“CODE”, “COIN”, “COMMUNICATION”, and the like. Depending upon thequantity of word objects 108 within any given data table, the data tablemay additionally include sub-data tables within which word objects 108are organized by prefixes that are three characters or more in length.Continuing onward with the foregoing example, if the “CO” data tableincluded, for instance, more than 256 word objects 108, the “CO” datatable would additionally include one or more sub-data tables of wordobjects 108 corresponding with the most frequently appearingthree-letter prefixes. By way of example, therefore, the “CO” data tablemay also include a “COM” sub-data table and a “CON” sub-data table. If asub-data table includes more than the predetermined number of wordobjects 108, for example a quantity of 256, the sub-data table mayinclude further sub-data tables, such as might be organized according toa four letter prefixes. It is noted that the aforementioned quantity of256 of the word objects 108 corresponds with the greatest numericalvalue that can be stored within one byte of the memory 20.

Accordingly, when, at 224, each prefix object is sought to be used toidentify a corresponding word object 108, and for instance the instantprefix object is “AP”, the “AP” data table will be consulted. Since allof the word objects 108 in the “AP” data table will correspond with theprefix object “AP”, the word object 108 in the “AP” data table withwhich is associated a frequency object 104 having a frequency valuerelatively higher than any of the other frequency objects 104 in the“AP” data table is identified. The identified word object 108 and theassociated frequency object 104 are then stored in a result registerthat serves as a result of the various comparisons of the generatedprefix objects with the contents of the memory 20.

It is noted that one or more, or possibly all, of the prefix objectswill be prefix objects for which a corresponding word object 108 is notidentified in the memory 20. Such prefix objects are considered to beorphan prefix objects and are separately stored or are otherwiseretained for possible future use. In this regard, it is noted that manyor all of the prefix objects can become orphan object if, for instance,the user is trying to enter a new word or, for example, if the user hasmis-keyed and no word corresponds with the mls-keyed input.

Once the result has been obtained at 224, the disambiguation function 22determines, as at 225, whether at least one language object 100 wasidentified as corresponding with a prefix object. If not, processingcontinues as at 226 where processing branches to FIG. 15A, which isdiscussed in greater detail elsewhere herein. If it is determined at 225that at least one language object 100 was identified as correspondingwith a prefix object, processing continues at 228 where thedisambiguation routine 22 begins to determine whether artificialvariants should be generated.

In order to determine the need for artificial variants, the process at228 branches, as at 230, to the artificial variant process depictedgenerally in FIG. 4 and beginning with the numeral 304. Thedisambiguation function then determines, as at 308, whether any of theprefix objects in the result correspond with what had been the defaultoutput 76 prior to detection of the current key input. If a prefixobject in the result corresponds with the previous default output, thismeans that the current input sequence corresponds with a word object 108and, necessarily, the previous default output also corresponded with aword object 108 during the previous disambiguation cycle within thecurrent session.

If it is determined at 308 that a prefix object in the resultcorresponds with what had been the default output 76 prior to detectionof the current key input, the next point of analysis is to determine, asat 310, whether the previous default output was made the default outputbecause of a selection input, such as would have caused the setting of aflag, such as at 254 of FIG. 3B, discussed in greater detail elsewhereherein. In the event that the previous default output was not the resultof a selection input, meaning that no flag was set, no artificialvariants are needed, and the process returns, as at 312, to the mainprocess at 232. However, if it is determined at 310 that the previousdefault output was the result of a selection input, then artificialvariants are generated, as at 316.

More specifically, each of the artificial variants generated at 316include the previous default output plus one of the characters 48assigned to the key 28 of the current input. As such, if the key 28 ofthe current input has two characters, i.e., a first character 52 and asecond character 56, two artificial variants will be generated at 316.One of the artificial variants will include the previous default outputplus the first character 52. The other artificial variant will includethe previous default output plus the second character 56.

However, if it is determined at 308 that none of the prefix objects inthe result correspond with the previous default output, it is nextnecessary to determine, as at 314, whether the previous default outputhad corresponded with a word object 108 during the previousdisambiguation cycle within the current session. If the answer to theinquiry at 314 is no, it is still necessary to determine, as at 318,whether the previous default output was made the default output becauseof a selection input, such as would have causes the setting of the flag.In the event that the previous default output was not the result of aselection input, no artificial variants are needed, and the processreturns, as at 312, to the main process at 232.

However, if it is determined at 318 that the previous default output wasthe result of a selection input, it is necessary to next determine as at319 whether the pre-selection default output, i.e., what had been thedefault output prior to the selection input that was identified at 318,corresponded with a word object 108. If so, artificial variants arecreated, as at 321, for the pre-selection default output plus each ofthe linguistic elements assigned to the key 28 of the current input.Processing thereafter continues to 316 where artificial variants aregenerated for the previous default output plus the linguistic elementsassigned to the key 28 of the current input. Alternatively, if at 319 itis determined that the pre-selection default output did not correspondwith a word object 108, processing continues directly to 316 whereartificial variants are generated for the previous default output plusthe linguistic elements assigned to the key 28 of the current input.

On the other hand, if it is determined that the answer to the inquiry at314 is yes, meaning that the previous default output had correspondedwith a word object, but with the current input the previous defaultoutput combined with the current input has ceased to correspond with anyword object 108, then artificial variants are generated, again as at316.

After the artificial variants are generated at 316, the method thendetermines, as at 320, whether the result includes any prefix objects atall. If not, processing returns, as at 312, to the main process at 232.However, if it is determined at 320 that the result includes at least afirst prefix object, meaning that the current input sequence correspondswith a word object 108, processing is transferred to 324 where anadditional artificial variant is created. Specifically, the prefixobject of the result with which is associated the frequency object 104having the relatively highest frequency value among the other frequencyobjects 104 in the result is identified, and the artificial variant iscreated by deleting the final character from the identified prefixobject and replacing it with an opposite character 48 on the same key 28of the current input that generated the final character 48 of theidentified prefix object. In the event that the specific key 28 has morethan two characters 48 assigned thereto, each such opposite character 48will be used to generate an additional artificial variant.

Once the need for artificial variants has been identified, as at 228,and such artificial variants have been generated, as in FIG. 4 and asdescribed above, processing continues, as at 232, where duplicate wordobjects 108 associated with relatively lower frequency values aredeleted from the result. Such a duplicate word object 108 could begenerated, for instance, by the frequency learning database 96, as willbe set forth in greater detail below. If a word object 108 in the resultmatches one of the artificial variants, the word object 108 and itsassociated frequency object 104 generally will be removed from theresult because the artificial variant will be assigned a preferredstatus in the output 64, likely in a position preferred to any wordobject 108 that might have been identified.

Once the duplicate word objects 108 and the associated frequency objects104 have been removed at 232, the remaining prefix objects are arranged,as at 236, in an output set in decreasing order of frequency value. Theorphan prefix objects mentioned above may also be added to the outputset, albeit at positions of relatively lower frequency value than anyprefix object for which a corresponding word object 108 was found. It isalso necessary to ensure that the artificial variants, if they have beencreated, are placed at a preferred position in the output set. It isunderstood that artificial variants may, but need not necessarily be,given a position of preference, i.e., assigned a relatively higherpriority or frequency, than prefix objects of the result.

If it is determined, as at 240, that the flag has been set, meaning thata user has made a selection input, either through an express selectioninput or through an alternation input of a movement input, then thedefault output 76 is considered to be “locked,” meaning that theselected variant will be the default prefix until the end of thesession. If it is determined at 240 that the flag has been set, theprocessing will proceed to 244 where the contents of the output set willbe altered, if needed, to provide as the default output 76 an outputthat includes the selected prefix object, whether it corresponds with aword object 108 or is an artificial variant. In this regard, it isunderstood that the flag can be set additional times during a session,in which case the selected prefix associated with resetting of the flagthereafter becomes the “locked” default output 76 until the end of thesession or until another selection input is detected.

Processing then continues, as at 248, to an output step after which anoutput 64 is generated as described above. More specifically, processingproceeds, as at 250, to the subsystem depicted generally in FIG. 6 anddescribed below. Processing thereafter continues at 204 where additionalinput is detected. On the other hand, if it is determined at 240 thatthe flag had not been set, then processing goes directly to 248 withoutthe alteration of the contents of the output set at 244.

The handheld electronic device 4 may be configured such that any orphanprefix object that is included in an output 64 but that is not selectedwith the next input is suspended. This may be limited to orphan prefixobjects appearing in the variant portion 80 or may apply to orphanprefix objects anywhere in the output 64. The handheld electronic device4 may also be configured to similarly suspend artificial variants insimilar circumstances. A reason for such suspension is that each suchorphan prefix object and/or artificial variant, as appropriate, mayspawn a quantity of offspring orphan prefix objects equal to thequantity of characters 48 on a key 28 of the next input. That is, eachoffspring will include the parent orphan prefix object or artificialvariant plus one of the characters 48 of the key 28 of the next input.Since orphan prefix objects and artificial variants substantially do nothave correspondence with a word object 108, spawned offspring objectsfrom parent orphan prefix objects and artificial variants likewise willnot have correspondence with a word object 108. Such suspended orphanprefix objects and/or artificial variants may be considered to besuspended, as compared with being wholly eliminated, since suchsuspended orphan prefix objects and/or artificial variants may reappearlater as parents of a spawned orphan prefix objects and/or artificialvariants, as will be explained below.

If the detected input is determined, as at 212, to be an operationalinput, processing then continues to determine the specific nature of theoperational input. For instance, if it is determined, as at 252, thatthe current input is a selection input, processing continues at 254. At254, the word object 108 and the associated frequency object 104 of thedefault portion 76 of the output 64, as well as the word object 108 andthe associated frequency object 104 of the portion of the variant output80 that was selected by the selection input, are stored in a temporarylearning data register. Additionally, the flag is set. Processing thenreturns to detection of additional inputs as at 204.

If it is determined, as at 260, that the input is a delimiter input,processing continues at 264 where the current session is terminated andprocessing is transferred, as at 266, to the learning functionsubsystem, as at 404 of FIG. 5A. A delimiter input would include, forexample, the actuation of a <SPACE> key 116, which would both enter adelimiter symbol and would add a space at the end of the word, actuationof the <ENTER> key 44, which might similarly enter a delimiter input andenter a space, and by a translation of the thumbwheel 32, such as isindicated by the arrow 38, which might enter a delimiter input withoutadditionally entering a space.

It is first determined, as at 408, whether the default output at thetime of the detection of the delimiter input at 260 matches a wordobject 108 in the memory 20. If it does not, this means that the defaultoutput is a user-created output that should be added to the new wordsdatabase 92 for future use. In such a circumstance processing thenproceeds to 412 where the default output is stored in the new wordsdatabase 92 as a new word object 108. Additionally, a frequency object104 is stored in the new words database 92 and is associated with theaforementioned new word object 108. The new frequency object 104 isgiven a relatively high frequency value, typically within the upperone-fourth or one-third of a predetermined range of possible frequencyvalues.

In this regard, frequency objects 104 are given an absolute frequencyvalue generally in the range of zero to 65,535. The maximum valuerepresents the largest number that can be stored within two bytes of thememory 20. The new frequency object 104 that is stored in the new wordsdatabase 92 is assigned an absolute frequency value within the upperone-fourth or one-third of this range, particularly since the new wordwas used by a user and is likely to be used again.

With further regard to frequency object 104, it is noted that within agiven data table, such as the “CO” data table mentioned above, theabsolute frequency value is stored only for the frequency object 104having the highest frequency value within the data table. All of theother frequency objects 104 in the same data table have frequency valuesstored as percentage values normalized to the aforementioned maximumabsolute frequency value. That is, after identification of the frequencyobject 104 having the highest frequency value within a given data table,all of the other frequency objects 104 in the same data table areassigned a percentage of the absolute maximum value, which representsthe ratio of the relatively smaller absolute frequency value of aparticular frequency object 104 to the absolute frequency value of theaforementioned highest value frequency object 104. Advantageously, suchpercentage values can be stored within a single byte of memory, thussaving storage space within the handheld electronic device 4.

Upon creation of the new word object 108 and the new frequency object104, and storage thereof within the new words database 92, processing istransferred to 420 where the learning process is terminated. Processingis then returned to the main process, as at 204.

If at 408 it is determined that the word object 108 in the defaultoutput 76 matches a word object 108 within the memory 20, processingthen continues at 416 where it is determined whether the aforementionedflag has been set, such as occurs upon the detection of a selectioninput, and alternation input, or a movement input, by way of example. Ifit turns out that the flag has not been set, this means that the userhas not expressed a preference for a variant prefix object over adefault prefix object, and no need for frequency learning has arisen. Insuch a circumstance, processing continues at 420 where the learningprocess is terminated. Processing then returns to the main process at204.

However, if it is determined at 416 that the flag has been set, theprocessor 20 retrieves from the temporary learning data register themost recently saved default and variant word objects 108, along withtheir associated frequency objects 104. It is then determined, as at428, whether the default and variant word objects 108 had previouslybeen subject of a frequency learning operation. This might bedetermined, for instance, by determining whether the variant word object108 and the associated frequency object 104 were obtained from thefrequency learning database 96. If the default and variant word objects108 had not previously been the subject of a frequency learningoperation, processing continues, as at 432, where the variant wordobject 108 is stored in the frequency learning database 96, and arevised frequency object 104 is generated having a frequency valuegreater than that of the frequency object 104 that previously had beenassociated with the variant word object 108. In the present exemplarycircumstance, i.e., where the default word object 108 and the variantword object 108 are experiencing their first frequency learningoperation, the revised frequency object 104 may, for instance, be givena frequency value equal to the sum of the frequency value of thefrequency object 104 previously associated with the variant word object108 plus one-half the difference between the frequency value of thefrequency object 104 associated with the default word object 108 and thefrequency value of the frequency object 104 previously associated withthe variant word object 108. Upon storing the variant word object 108and the revised frequency object 104 in the frequency learning database96, processing continues at 420 where the learning process is terminatedand processing returns to the main process, as at 204.

If it is determined at 428 that that default word object 108 and thevariant word object 108 had previously been the subject of a frequencylearning operation, processing continues to 436 where the revisedfrequency value 104 is instead given a frequency value higher than thefrequency value of the frequency object 104 associated with the defaultword object 108. After storage of the variant word object 108 and therevised frequency object 104 in the frequency learning database 96,processing continues to 420 where the learning process is terminated,and processing then returns to the main process, as at 204.

With further regard to the learning function, it is noted that thelearning function additionally detects whether both the default wordobject 108 and the variant word object 104 were obtained from thefrequency learning database 96. In this regard, when word objects 108are identified, as at 224, for correspondence with generated prefixobjects, all of the data sources in the memory are polled for suchcorresponding word objects 108 and corresponding frequency objects 104.Since the frequency learning database 96 stores word objects 108 thatalso are stored either in the generic word list 88 or the new wordsdatabase 92, the word object 108 and the associated frequency object 104that are obtained from the frequency learning database 96 typically areduplicates of word objects 108 that have already been obtained from thegeneric word list 88 or the new words database 92. However, theassociated frequency object 104 obtained from the frequency learningdatabase 96 typically has a frequency value that is of a greatermagnitude than that of the associated frequency object 104 that had beenobtained from the generic word list 88. This reflects the nature of thefrequency learning database 96 as imparting to a frequently used wordobject 108 a relatively greater frequency value than it otherwise wouldhave in the generic word list 88.

It thus can be seen that the learning function indicated in FIGS. 5A and5B and described above is generally not initiated until a delimiterinput is detected, meaning that learning occurs only once for eachsession. Additionally, if the final default output is not a user-definednew word, the word objects 108 that are the subject of the frequencylearning function are the word objects 108 which were associated withthe default output 76 and the selected variant output 80 at the timewhen the selection occurred, rather than necessarily being related tothe object that ultimately resulted as the default output at the end ofthe session. Also, if numerous learnable events occurred during a singlesession, the frequency learning function operates only on the wordobjects 108 that were associated with the final learnable event, i.e., aselection event, an alternation event, or a movement event, prior totermination of the current session.

With further regard to the identification of various word objects 108for correspondence with generated prefix objects, it is noted that thememory 20 can include a number of additional data sources 99 in additionto the generic word list 88, the new words database 92, and thefrequency learning database 96, all of which can be consideredlinguistic sources. An exemplary two other data sources 99 are depictedin FIG. 2A, it being understood that the memory 20 might include anynumber of other data sources 99. The other data sources 99 mightinclude, for example, an address database, a speed-text database, or anyother data source without limitation. An exemplary speed-text databasemight include, for example, sets of words or expressions or other datathat are each associated with, for example, a character string that maybe abbreviated. For example, a speed-text database might associate thestring “br” with the set of words “Best Regards”, with the intentionthat a user can type the string “br” and receive the output “BestRegards”.

In seeking to identify word objects 108 that correspond with a givenprefix object, the handheld electronic device 4 may poll all of the datasources in the memory 20. For instance the handheld electronic device 4may poll the generic word list 88, the new words database 92, thefrequency learning database 96, and the other data sources 99 toidentify word objects 108 that correspond with the prefix object. Thecontents of the other data sources 99 may be treated as word objects108, and the processor 16 may generate frequency objects 104 that willbe associated such word objects 108 and to which may be assigned afrequency value in, for example, the upper one-third or one-fourth ofthe aforementioned frequency range. Assuming that the assigned frequencyvalue is sufficiently high, the string “br”, for example, wouldtypically be output to the display 60. If a delimiter input is detectedwith respect to the portion of the output having the association withthe word object 108 in the speed-text database, for instance “br”, theuser would receive the output “Best Regards”, it being understood thatthe user might also have entered a selection input as to the exemplarystring “br”.

The contents of any of the other data sources 99 may be treated as wordobjects 108 and may be associated with generated frequency objects 104having the assigned frequency value in the aforementioned upper portionof the frequency range. After such word objects 108 are identified, thenew word learning function can, if appropriate, act upon such wordobjects 108 in the fashion set forth above.

Again regarding FIG. 3A, when processing proceeds to the filtrationstep, as at 232, and the duplicate word objects 108 and the associatedfrequency objects 104 having relatively lower frequency values arefiltered, the remaining results may include a variant word object 108and a default word object 108, both of which were obtained from thefrequency learning database 96. In such a situation, it can beenvisioned that if a user repetitively and alternately uses one wordthen the other word, over time the frequency objects 104 associated withsuch words will increase well beyond the aforementioned maximum absolutefrequency value for a frequency object 104. Accordingly, if it isdetermined that both the default word object 108 and the variant wordobject 108 in the learning function were obtained from the frequencylearning database 96, instead of storing the variant word object 108 inthe frequency learning database 96 and associating it with a frequencyobject 104 having a relatively increased frequency value, instead thelearning function stores the default word object 108 and associates itwith a revised frequency object 104 having a frequency value that isrelatively lower than that of the frequency object 104 that isassociated with the variant word object 108. Such a schemeadvantageously avoids excessive and unnecessary increases in frequencyvalue.

If it is determined, such as at 268, that the current input is amovement input, such as would be employed when a user is seeking to editan object, either a completed word or a prefix object within the currentsession, the caret 84 is moved, as at 272, to the desired location, andthe flag is set, as at 276. Processing then returns to where additionalinputs can be detected, as at 204.

In this regard, it is understood that various types of movement inputscan be detected from the input device 8. For instance, a rotation of thethumbwheel 32, such as is indicated by the arrow 34 of FIG. 1, couldprovide a movement input, as could the actuation of the <NEXT> key 40,or other such input, potentially in combination with other devices inthe input apparatus 8. In the instance where such a movement input isdetected, such as in the circumstance of an editing input, the movementinput is additionally detected as a selection input. Accordingly, and asis the case with a selection input such as is detected at 252, theselected variant is effectively locked with respect to the defaultportion 76 of the output 64. Any default output 76 during the samesession will necessarily include the previously selected variant.

In the context of editing, however, the particular displayed object thatis being edited is effectively locked except as to the character that isbeing edited. In this regard, therefore, the other characters of theobject being edited, i.e., the characters that are not being edited, aremaintained and are employed as a context for identifying additional wordobjects 108 and the like that correspond with the object being edited.Were this not the case, a user seeking to edit a letter in the middle ofa word otherwise likely would see as a new output 64 numerous objectsthat bear little or no resemblance to the characters of the object beingedited since, in the absence of maintaining such context, an entirelynew set of prefix objects including all of the permutations of thecharacters of the various keystrokes of the object being edited wouldhave been generated. New word objects 108 would have been identified ascorresponding with the new prefix objects, all of which couldsignificantly change the output 64 merely upon the editing of a singlecharacter. By maintaining the other characters currently in the objectbeing edited, and employing such other characters as contextinformation, the user can much more easily edit a word that is depictedon the display 60.

In the present exemplary embodiment of the handheld electronic device 4,if it is determined, as at 252, that the input is not a selection input,and it is determined, as at 260, that the input is not a delimiterinput, and it is further determined, as at 268, that the input is not amovement input, in the current exemplary embodiment of the handheldelectronic device 4 the only remaining operational input generally is adetection of the <DELETE> key 86 of the keys 28 of the keypad 24. Upondetection of the <DELETE> key 86, the final character of the defaultoutput is deleted, as at 280. At this point, the processing generallywaits until another input is detected, as at 284. It is then determined,as at 288, whether the new input detected at 284 is the same as the mostrecent input that was related to the final character that had just beendeleted at 280. If so, the default output 76 is the same as the previousdefault output, except that the last character is the opposite characterof the key actuation that generated the last character. Processing thencontinues to 292 where learning data, i.e., the word object 108 and theassociate frequency object 104 associated with the previous defaultoutput 76, as well as the word object 108 and the associate frequencyobject 104 associated with the new default output 76, are stored in thetemporary learning data register and the flag is set. Such a keysequence, i.e., an input, the <DELETE> key 86, and the same input asbefore, is an alternation input. Such an alternation input replaces thedefault final character with an opposite final character of the key 28which generated the final character 48 of the default output 76. Thealternation input is treated as a selection input for purposes oflocking the default output 76 for the current session, and also triggersthe flag which will initiate the learning function upon detection of adelimiter input at 260.

If it turns out, however, that the system detects at 288 that the newinput detected at 284 is different than the input immediately prior todetection of the <DELETE> key 86, processing continues at 212 where theinput is determined to be either an operational input or an input of akey having one or more characters 48, and processing continuesthereafter.

It is also noted that when the main process reaches the output stage at248, an additional process is initiated which determines whether thevariant component 72 of the output 64 should be initiated. Processing ofthe additional function is initiated from 250 at element 504 of FIG. 6.Initially, the method at 508 outputs the text component 68 of the output64 to the display 60. Further processing determines whether or not thevariant component 72 should be displayed.

Specifically, it is determined, as at 512, whether the variant component72 has already been displayed during the current session. If the variantcomponent 72 has already been displayed, processing continues at 516where the new variant component 72 resulting from the currentdisambiguation cycle within the current session is displayed. Processingthen returns to a termination point at 520, after which processingreturns to the main process at 204. If, however, it is determined at 512that the variant component 72 has not yet been displayed during thecurrent session, processing continues, as at 524, to determine whetherthe elapsed time between the current input and the immediately previousinput is longer than a predetermined duration. If it is longer, thenprocessing continues at 516 where the variant component 72 is displayedand processing returns, through 520, to the main process, as at 204.However, if it is determined at 524 that the elapsed time between thecurrent input and the immediately previous input is less than thepredetermined duration, the variant component 72 is not displayed, andprocessing returns to the termination point at 520, after whichprocessing returns to the main process, as at 204.

Advantageously, therefore, if a user is entering keystrokes relativelyquickly, the variant component 72 will not be output to the display 60,where it otherwise would likely create a visual distraction to a userseeking to enter keystrokes quickly. If at any time during a givensession the variant component 72 is output to the display 60, such as ifthe time between successive inputs exceeds the predetermined duration,the variant component 72 will continue to be displayed throughout thatsession. However, upon the initiation of a new session, the variantcomponent 72 will be withheld from the display if the user consistentlyis entering keystrokes relatively quickly.

An exemplary input sequence is depicted in FIGS. 1 and 7-11. In thisexample, the user is attempting to enter the word “APPLOADER”, and thisword presently is not stored in the memory 20. In FIG. 1 the user hasalready typed the “AS” key 28. Since the data tables in the memory 20are organized according to two-letter prefixes, the contents of theoutput 64 upon the first keystroke are obtained from the N-gram objects112 within the memory. The first keystroke “AS” corresponds with a firstN-gram object 112 “S” and an associated frequency object 104, as well asanother N-gram object 112 “A” and an associated frequency object 104.While the frequency object 104 associated with “S” has a frequency valuegreater than that of the frequency object 104 associated with “A”, it isnoted that “A” is itself a complete word. A complete word is alwaysprovided as the default output 76 in favor of other prefix objects thatdo not match complete words, regardless of associated frequency value.As such, in FIG. 1, the default portion 76 of the output 64 is “A”.

In FIG. 7, the user has additionally entered the “OP” key 28. Thevariants are depicted in FIG. 7. Since the prefix object “SO” is also aword, it is provided as the default output 76. In FIG. 8, the user hasagain entered the “OP” key 28 and has also entered the “L” key 28. It isnoted that the exemplary “L” key 28 depicted herein includes only thesingle character 48 “L”.

It is assumed in the instant example that no operational inputs havethus far been detected. The default output 76 is “APPL”, such as wouldcorrespond with the word “APPLE”. The prefix “APPL” is depicted both inthe text component 68, as well as in the default portion 76 of thevariant component 72. Variant prefix objects in the variant portion 80include “APOL”, such as would correspond with the word “APOLOGIZE”, andthe prefix “SPOL”, such as would correspond with the word “SPOLIATION”.

It is particularly noted that the additional variants “AOOL”, “AOPL”,“SOPL”, and “SOOL” are also depicted as variants 80 in the variantcomponent 72. Since no word object 108 corresponds with these prefixobjects, the prefix objects are considered to be orphan prefix objectsfor which a corresponding word object 108 was not identified. In thisregard, it may be desirable for the variant component 72 to include aspecific quantity of entries, and in the case of the instant exemplaryembodiment the quantity is seven entries. Upon obtaining the result at224, if the quantity of prefix objects in the result is fewer than thepredetermined quantity, the disambiguation function will seek to provideadditional outputs until the predetermined number of outputs areprovided. In the absence of artificial variants having been created, theadditional variant entries are provided by orphan prefix objects. It isnoted, however, that if artificial variants had been generated, theylikely would have occupied a place of preference in favor of such orphanprefix objects, and possibly also in favor of the prefix objects of theresult.

It is further noted that such orphan prefix objects may actually beoffspring orphan prefix objects from suspended parent orphan prefixobjects and/or artificial variants. Such offspring orphan prefix objectscan be again output depending upon frequency ranking as explained below,or as otherwise ranked.

The orphan prefix objects are ranked in order of descending frequencywith the use of the N-gram objects 112 and the associated frequencyobjects 104. Since the orphan prefix objects do not have a correspondingword object 108 with an associated frequency object 104, the frequencyobjects 104 associated with the various N-gram objects 112 must beemployed as a fallback.

Using the N-gram objects 112, the disambiguation function first seeks todetermine if any N-gram object 112 having, for instance, threecharacters is a match for, for instance, a final three characters of anyorphan prefix object. The example of three characters is given since theexemplary embodiment of the handheld electronic device 4 includes N-gramobjects 112 that are an exemplary maximum of the three characters inlength, but it is understood that if the memory 20 included N-gramobjects four characters in length or longer, the disambiguation functiontypically would first seek to determine whether an N-gram object havingthe greatest length in the memory 20 matches the same quantity ofcharacters at the end of an orphan prefix object.

If only one prefix object corresponds in such a fashion to a threecharacter N-gram object 112, such orphan prefix object is listed firstamong the various orphan prefix objects in the variant output 80. Ifadditional orphan prefix objects are matched to N-gram objects 112having three characters, then the frequency objects 104 associated withsuch identified N-gram objects 112 are analyzed, and the matched orphanprefix objects are ranked amongst themselves in order of decreasingfrequency.

If it is determined that a match cannot be obtained with an N-gramobject 112 having three characters, then two-character N-gram objects112 are employed. Since the memory 20 includes all permutations oftwo-character N-gram objects 112, a last two characters of each orphanprefix object can be matched to a corresponding two-character N-gramobject 112. After such matches are achieved, the frequency objects 104associated with such identified N-gram objects 112 are analyzed, and theorphan prefix objects are ranked amongst themselves in descending orderof frequency value of the frequency objects 104 that were associatedwith the identified N-gram objects 112. It is further noted thatartificial variants can similarly be rank ordered amongst themselvesusing the N-gram objects 112 and the associated frequency objects 104,and such artificial variants can be suppressed from the output inappropriate circumstances, as set forth in greater detail below.

In FIG. 9 the user has additionally entered the “OP” key 28. In thiscircumstance, and as can be seen in FIG. 9, the default portion 76 ofthe output 64 has become the prefix object “APOLO” such as wouldcorrespond with the word “APOLOGIZE”, whereas immediately prior to thecurrent input the default portion 76 of the output 64 of FIG. 8 was“APPL” such as would correspond with the word “APPLE.” Again, assumingthat no operational inputs had been detected, the default prefix objectin FIG. 9 does not correspond with the previous default prefix object ofFIG. 8. As such, the first artificial variant “APOLP” is generated andin the current example is given a preferred position. The aforementionedartificial variant “APOLP” is generated by deleting the final characterof the default prefix object “APOLO” and by supplying in its place anopposite character 48 of the key 28 which generated the final characterof the default portion 76 of the output 64, which in the current exampleof FIG. 9 is “P”, so that the aforementioned artificial variants is“APOLP”.

Furthermore, since the previous default output “APPL” corresponded witha word object 108, such as the word object 108 corresponding with theword “APPLE”, and since with the addition of the current input theprevious default output “APPL” no longer corresponds with a word object108, two additional artificial variants are generated. One artificialvariant is “APPLP” and the other artificial variant is “APPLO”, andthese correspond with the previous default output “APPL” plus thecharacters 48 of the key 28 that was actuated to generate the currentinput. These artificial variants are similarly output as part of thevariant portion 80 of the output 64.

As can be seen in FIG. 9, the default portion 76 of the output 64“APOLO” no longer seems to match what would be needed as a prefix for“APPLOADER”, and the user likely anticipates that the desired word“APPLOADER” is not already stored in the memory 20. As such, the userprovides a selection input, such as by scrolling with the thumbwheel 32,or by actuating the <NEXT> key 40, until the variant string “APPLO” ishighlighted. The user then continues typing and enters the “AS” key.

The output 64 of such action is depicted in FIG. 10. Here, the string“APPLOA” is the default portion 76 of the output 64. Since the variantstring “APPLO” became the default portion 76 of the output 64 (notexpressly depicted herein) as a result of the selection input as to thevariant string “APPLO”, and since the variant string “APPLO” does notcorrespond with a word object 108, the character strings “APPLOA” and“APPLOS” were created as an artificial variants. Additionally, since theprevious default of FIG. 9, “APOLO” previously had corresponded with aword object 108, but now is no longer in correspondence with the defaultportion 76 of the output 64 of FIG. 10, the additional artificialvariants of “APOLOA” and “APOLOS” were also generated. Such artificialvariants are given a preferred position in favor of the three displayedorphan prefix objects.

Since the current input sequence in the example no longer correspondswith any word object 108, the portions of the method related toattempting to find corresponding word objects 108 are not executed withfurther inputs for the current session. That is, since no word object108 corresponds with the current input sequence, further inputs willlikewise not correspond with any word object 108. Avoiding the search ofthe memory 20 for such nonexistent word objects 108 saves time andavoids wasted processing effort.

As the user continues to type, the user ultimately will successfullyenter the word “APPLOADER” and will enter a delimiter input. Upondetection of the delimiter input after the entry of “APPLOADER”, thelearning function is initiated. Since the word “APPLOADER” does notcorrespond with a word object 108 in the memory 20, a new word object108 corresponding with “APPLOADER” is generated and is stored in the newwords database 92, along with a corresponding new frequency object 104which is given an absolute frequency in the upper, say, one-third orone-fourth of the possible frequency range. In this regard, it is notedthat the new words database 92 and the frequency learning database 96are generally organized in two-character prefix data tables similar tothose found in the generic word list 88. As such, the new frequencyobject 104 is initially assigned an absolute frequency value, but uponstorage the absolute frequency value, if it is not the maximum valuewithin that data table, will be changed to include a normalizedfrequency value percentage normalized to whatever is the maximumfrequency value within that data table.

As a subsequent example, in FIG. 11 the user is trying to enter the word“APOLOGIZE”. The user has entered the key sequence “AS” “OP” “OP” “L”“OP”. Since “APPLOADER” has now been added as a word object 108 to thenew words database 92 and has been associated with frequency object 104having a relatively high frequency value, the prefix object “APPLO”which corresponds with “APPLOADER” has been displayed as the defaultportion 76 of the output 64 in favor of the variant prefix object“APOLO”, which corresponds with the desired word “APOLOGIZE.” Since theword “APOLOGIZE” corresponds with a word object 108 that is stored atleast in the generic word list 88, the user can simply continue to enterkeystrokes corresponding with the additional letters “GIZE”, which wouldbe the letters in the word “APOLOGIZE” following the prefix object“APOLO”, in order to obtain the word “APOLOGIZE”. Alternatively, theuser may, upon seeing the output 64 depicted in FIG. 11, enter aselection input to affirmatively select the variant prefix object“APOLO”. In such a circumstance, the learning function will be triggeredupon detection of a delimiter symbol, and the word object 108 that hadcorresponded with the character string “APOLO” at the time the selectioninput was made will be stored in the frequency learning database 96 andwill be associated with a revised frequency object 104 having arelatively higher frequency value that is similarly stored in thefrequency learning database 96.

An additional feature of the handheld electronic device 4 is depictedgenerally in FIG. 12. In some circumstances, it is desirable that thedisambiguation function be disabled. For instance, when it is desired toenter a password, disambiguation typically is relatively more cumbersomethan during ordinary text entry. As such, when the system focus is onthe component corresponding with the password field, the componentindicates to the API that special processing is requested, and the APIdisables the disambiguation function and instead enables, for instance,a multi-tap input interpretation system. Alternatively, other inputinterpretation systems could include a chording system or apress-and-hold/press-and-release interpretation system. As such, whilean input entered with the disambiguation function active is an ambiguousinput, by enabling the alternative interpretation system, such as theexemplary multi-tap system, each input can be largely unambiguous.

As can be understood from FIG. 12, each unambiguous input is displayedfor a very short period of time within the password field 120, and isthen replaced with another output, such as the asterisk. The character“R” is shown displayed, it being understood that such display is onlyfor a very short period of time.

As can be seen in FIGS. 1 and 7-11, the output 64 includes the displayedgraphic 46 near the lower end of the variant component 72, and that thedisplayed graphic 46 is highly similar to the graphic 42 of the <NEXT>key 40. Such a depiction provides an indication to the user which of thekeys 28 of the keypad 24 can be actuated to select a variant output. Thedepiction of the displayed graphic 46 provides an association betweenthe output 64 and the <NEXT> key 40 in the user's mind. Additionally, ifthe user employs the <NEXT> key 40 to provide a selection input, theuser will be able to actuate the <NEXT> key 40 without moving the user'shands away from the position the hands were in with respect to thehousing 6 during text entry, which reduces unnecessary hand motions,such as would be required if a user needed to move a hand to actuate thethumbwheel 32. This saves time and effort.

It is noted that the layout of the characters 48 disposed on the keys 28in FIG. 1 is an exemplary character layout that would be employed wherethe intended primary language used on the handheld electronic device 4was, for instance, English. Other layouts involving these characters 48and/or other characters can be used depending upon the intended primarylanguage and any language bias in the makeup of the language objects100.

The map file 49 depicted in FIG. 2A is depicted in greater detail inFIG. 13. The map file 49 is a table that includes an indication of thekeys 28 and the characters 48 assigned thereto. As can be seen in FIG.13, many of the keys 28 have characters 48 assigned thereto in additionto those characters 48 that are depicted in FIG. 1 as being disposed onthe keys 28. For example, the map file 49 indicates that the <UI> key 28has assigned thereto the letters “U” and “I”, and such letters areindicated in FIG. 1 as being characters 48 disposed on the <UI> key 28.FIG. 13 further indicates that the <UI> key 28 additionally has assignedthereto the characters 48 Ù, Ú, Û, Ü, Í, Ì, Î, and Ï. It is noted thatfor the sake of simplicity the characters 48 are depicted in FIGS. 13-16as being capital letter characters. It is further noted, however, thatthe characters 48 could additionally include lower case lettercharacters or other characters without departing from the presentconcept.

While the keys 28 have assigned thereto the characters 48 depicted inthe map file 49, not all of the characters 48 necessarily are active onthe handheld electronic device 4. That is, even though the characters 48U, I, Ù, Ú, Û, Ü, Í, Ì, Î, and Ï are assigned to the <UI> key 28, notall of these characters 48 are automatically employed in, for instance,the generation of prefix objects for the purpose of disambiguating anambiguous input. An active character 48 is a character 48 that isassigned to a key 28 and that is considered by the processor apparatusto be a possible intended result of actuating the key 28 during a textentry procedure, although limitations can be imposed upon thecircumstances wherein a character 48 is considered to be “active”.

The characters 48 that are active on the handheld electronic device 4are included in an alphabet 45, such as is depicted in FIG. 14, that isstored in the memory 20. In the present exemplary embodiment, thealphabet 45 includes a static portion 51 that is stored as a part of thegeneric word list 88 and a modifiable portion 47 that is stored as apart of the new words database 92. The static portion 51 could be saidto represent a core alphabet, which would be at least a portion of thealphabet 45.

The modifiable portion 47 of the alphabet 45 is advantageouslyconfigured to allow the addition to the alphabet 45 of characters 48from the map file 49 that are not, for instance, already included in thestatic portion 51 of the alphabet 45. The modifiable portion 47 thuscould be said to represent an extended alphabet, which would be at leasta portion of the alphabet 45.

It can be seen that at least some of the characters 48 in the map file49 are in the alphabet 45. As a general matter, the language objects 100stored in the memory 20 are comprised of characters 48 in the alphabet45.

Upon the detection of an ambiguous input, the processor apparatusconsults the map file 49 to identify the set of characters 48 that areassigned to the keys 28 of the ambiguous input. The set of characters 48from the map file 49 are then compared with the alphabet 45 to identifythe characters 48 in the set that are also in the alphabet 45. Statedotherwise, the map file 49 provides a listing of all of the characters48 assigned to the keys 28 of the ambiguous input, and the alphabet 45allows the identification of the characters 48 that are active on thehandheld electronic device 4. In comparing the set of characters 48 fromthe map file 49 with those of the alphabet 45, the set of characters 48typically will be compared with both the static portion 51 and themodifiable portion 47 of the alphabet 45 to obtain all active characters48, although this need not always be the case.

As a general matter, the static portion 51 is unchangeable and reflectsthe various characters 48 of which the language objects 100 in thegeneric word list 88 are comprised. The static portion 51 thus isindicative of the various characters 48 that typically would beconsidered to be valid characters in the language of the generic wordlist 88. For instance, the language of the generic word list 88 may beEnglish, such as might be indicated by a relatively large proportion ofEnglish words being reflected as language objects 100 stored in thegeneric word list 88. The resultant static portion 51 of the alphabet 45thus might comprise the twenty-six Latin letters.

The modifiable portion 47 of the alphabet 45 generally reflects theadditional characters 48 that are not already a part of the staticportion 51 and that, for instance, comprise the characters 48 in one ormore of the language objects 100 in, for instance, the new wordsdatabase 92. In the exemplary alphabet 45 depicted in FIG. 14, themodifiable portion 47 thereof is indicated as including the extendedcharacter “E”. For instance, the user may have previously entered thenew language object 100 “SOUFFLE”. Upon entry of the new language object100 “SOUFFLE”, the extended character “E” would have been added to themodifiable portion 47 of the alphabet 45. In such a fashion, thecharacter “E” has been made an active character 48 on the handheldelectronic device 4.

The exemplary modifiable portion 47 depicted generally in FIG. 14 isshown as including only the extended character “E”. When implemented,however, the modifiable portion 47 is likely to additionally includesome or all of the characters 48 in the core alphabet, as already storedin the static portion 51. This is because the language objects 100stored in the new words database 92 typically would comprise bothextended characters and characters 48 in the core alphabet. Forinstance, the language object 100 “SOUFFLE” stored in the new wordsdatabase 92 is comprised of the characters 48 “S”, “O”, “U”, “F”, and“L” from the core alphabet and the extended character “E” from theextended alphabet. A language object 100 is stored in the new wordsdatabase 92 by indexing each character of the language object 100 to thecorresponding character of the extended alphabet as stored in themodifiable portion 47. The language objects 100 stored in the genericword list 88 are stored in a similar fashion with indexing of thecharacters of the language objects 100 to the characters 48 of the corealphabet as stored in the static portion 51. In order for the languageobject 100 “SOUFFLE” to be stored in the new words database 92, thecharacters 48 “S”, “0”, “U”, “F”, and “L” from the core alphabet mustadditionally be stored in the modifiable portion 47 as a part of theextended alphabet. While the extended alphabet stored in the modifiableportion 47 thus will almost certainly include characters 48 from thecore alphabet in addition to the exemplary extended character depictedin FIG. 14, it is noted that for the sake of simplicity only theextended characters of the modifiable portion 47 are depicted in FIG.14.

An exemplary text entry procedure is indicated in FIGS. 15A-15C. If itassumed that the alphabet 45 is that depicted generally in FIG. 14, anordinary actuation, i.e., a press-and-release actuation, of the key 28<UI> will result in an output such as that depicted generally in FIG.15A. That is, the character 48 “I” will be displayed as a text component68 and as a default portion 76 of a variant component 72. The character48 “U” is depicted as being the variant portion 80 of the variantcomponent 72.

If the user is seeking to enter the language object 100 “ÜBER”, neitherof the characters 48 “I” and “U” in the variant component 72 of FIG. 15Awill be an acceptable first character. The user can, however, displaythe set of characters 48 from the map file 49 that are assigned to thekey 28 <UI> by actuating the key 28 <UI> with a press-and-hold actuationand by performing a scrolling operation with the thumbwheel 32. Such anoutput is depicted generally in FIG. 15B, it being noted that only aportion of the set of characters 48 is depicted in the variant component72, with the graphic 46 being depicted in the variant component 72 asindicating the existence of additional variants in the forms of othercharacters 48 from the map file 49 that are assigned to the key 28 <UI>.

In FIG. 15B, the character 48 “Ï” is depicted as being the defaultportion 76 of the variant component 72, and is additionally depicted asbeing the text component 68. FIG. 15C depicts that the user has entereda navigational input, such as by scrolling the thumbwheel 32 oractuating the <NEXT> 40 sufficiently that the character 48 “Ü” ishighlighted and is displayed as the text component 68.

In order the complete the entry of the new language object 100 “ÜBER”,the user will thereafter need to actuate the keys 28 <BN>, <ER>, and<ER>, although since a language object 100 for the word “ÜBER” is notalready stored in the memory 20, the user likely will have to expresslyenter the additionally characters 48 of “ÜBER”, such as with the use ofscrolling among the variants 80 after some of the keystrokes. Uponentry, for example, of the new language object “ÜBER”, the character 48“Ü” is added to the modifiable portion 47 of the alphabet 45, as isdepicted generally in FIG. 16, and a language object 100 for “ÜBER” hasbeen added to the new words database 92. Although not expressly depictedherein, the characters 48 “B”, “E”, and “R” might also need to be addedto the modifiable portion 47 if not already stored therein.

The character 48 “Ü” has thus been made an active character 48 on thehandheld electronic device 4. Accordingly, future entry of the word“ÜBER” will advantageously be much easier for the user since “Ü” hasbeen made an active character 48 on the handheld electronic device 4 andthus will be employed by the processor apparatus in seeking todisambiguate an ambiguous input, and since a language object 100 for“ÜBER” has been stored in the memory 20.

It thus can be seen that the handheld electronic device 4 is configuredto allow dynamic expansion of the set of characters 48 that are activethereon to enable the entry of new language objects 100 havingcharacters 48 that are not already active on the handheld electronicdevice 4. This allows enhanced utility and customizability to the needsof the user.

It is noted, however, that the static portion 51 and the modifiableportion 47 need not always be consulted during all text-relatedoperations on the handheld electronic device 4. For instance, in thepresent exemplary embodiment, artificial variants are comprised only ofcharacters 48 in the core alphabet. That is, in generating artificialvariants, the only characters 48 that are considered to be active on thehandheld electronic device 4 are the characters 48 in the core alphabet,i.e., those characters 48 stored in the static portion 51. This can beaccomplished by, for instance, when an artificial variant is generatedas including an initial portion and one or more of the characters 48assigned to the current key 28, only the static portion 51 is consultedto determined the “effectively” active characters 48 assigned to thecurrent key 28. This advantageously assists in avoiding the undesirablegeneration of artificial variants having a low likelihood of being theentry desired by a user. The limitation of artificial variants tocharacters of the core alphabet can be implemented in any of a varietyof ways.

In order to further avoid the generation of artificial variants having alow likelihood or no likelihood of being the entry desired by a user,each proposed artificial variant is sought to be compared with one ormore N-gram objects 112 in the memory 20 prior to being output. That is,an artificial variant generated as described herein on the exemplaryhandheld electronic device 4 is merely a “proposed” artificial variantuntil a comparison can be attempted with one or more of the N-gramobjects 112. This is done, for example, in order to gauge whether or notthe proposed artificial variant is an unlikely variant or is a variantthat does not exist in the relevant language and should, for instance,be suppressed from the output. Suppression of an unlikely artificialvariant from an output is desirable since an artificial variant can beoutput at a position of relatively high priority, potentially at aposition of higher priority than a generated prefix object for which alanguage object 100 was identified in the memory 20.

For instance, if a particular artificial variant corresponds with anN-gram object 112 that is associated with a frequency object 104 havinga relatively low frequency value, such as a frequency value below apredetermined threshold, this would indicate that the particularartificial variant is extremely unlikely to be the entry desired by theuser. That is, since the frequency value of a frequency object 104associated with an N-gram object 112 indicates the relative probabilitythat the character string represented by that particular N-gram object112 exists at any location within any word of the relevant language, thecorrespondence of a low-probability N-gram 112 with an artificialvariant indicates of a low-probability artificial variant. Alow-probability artificial variant is desirably suppressed rather thanbeing output.

Similarly, if no N-gram object 112 can be found that corresponds with atleast a portion of a particular artificial variant, this would alsoindicate a low probability or a zero probability artificial variant. Inthe present exemplary embodiment, the memory 20 has stored therein manyof the three-character permutations of the twenty-six Latin letters andall of the two-character permutations of the twenty-six Latin letters.An artificial variant is compared with N-gram objects 112 by determiningwhether a 3-gram N-gram object 112 corresponds with a final threecharacters of the artificial variant. If no 3-gram N-gram object 112 canbe identified as corresponding with a final three characters of theartificial variant, the artificial variant is assigned a zeroprobability and is suppressed from the output. If an identified 3-gramN-gram object 112 is associated with a frequency object 104 having afrequency value below a predetermined threshold, the artificial variantwill be suppressed from the output. An artificial variant will be outputonly if a final three characters of the artificial variant correspondwith a 3-gram N-gram object 112 associated with a frequency object 104having a frequency value above the predetermined threshold. Thepredetermined threshold can be set as desired and might be, forinstance, in the upper half of the possible range of frequency values.

If the handheld electronic device 4 is configured to generate artificialvariants having only two characters, such artificial variants would becompared with 2-gram N-gram objects 112 to determine a frequency value.If the frequency value is below a predetermined threshold, theartificial variant will be suppressed from the output.

The exemplary disambiguation routine 22 of the exemplary handheldelectronic device 4 advantageously enables spelling substitution if, ina given language, a known spelling substitution exists. An example ofsuch a spelling substitution is the equivalence in the German languageof a double-s “ss” and a scharfes s or sharp s “β”. In accordance withreforms in the German language introduced in 1996, for instance, theformer “daβ”, i.e., “that”, should now be spelled “dass”, with the “ss”being substituted for the “β”. For any of a variety of reasons, thememory 20 may have stored therein a language object 100 representativeof only one of the two equivalent spellings of a given word.

If it is assumed that the active language on the handheld electronicdevice 4 is German, or if the German language is the only availablelanguage on the handheld electronic device 4, the handheld electronicdevice will also have stored thereon the aforementioned spellingsubstitution of “ss” and “C” that is specific to the German language. Inresponse to entering an ambiguous input, the disambiguation routine 22would generate a number of prefix objects corresponding with theambiguous input as described herein. If any prefix object is determinedto not correspond with any word object 108 and is thus an orphan prefixobject, and if the orphan prefix object includes a character string forwhich a known spelling substitution exists in the given language, thedisambiguation routine will generate an additional prefix object in thenature of the orphan prefix object with the spelling substitution.

For instance, if a user seeking the enter the German word “dass” enteredthe keystrokes <DF><AS><AS><AS>, and if the memory 20 had stored thereina word object 108 for “daβ” but not for “dass”, the prefix object havingthe spelling “dass” would be determined to be an orphan prefix object,it being assumed that no other word object 108 on the handheldelectronic device 4 corresponded with a word starting with “dass” andhaving additional characters. The disambiguation routine 22 would,however, determine that the “ss” character string of the orphan prefixobject “dass” had a known spelling substitution, specifically “B”.

The disambiguation routine thus would generate an additional prefixobject with the spelling “daβ”, and the word object 108 correspondingwith “daβ” would be identified as corresponding with the input.Advantageously, the disambiguation routine 22 would provide an outputconsistent with the ambiguous input entered by the user, rather thannecessarily being consistent with the identified word object 108. Thatis, in response to the entered the keystrokes <DF> <AS> <AS> <AS>, theproposed output in the present example would be “dass”, as is indicatedgenerally in FIG. 17, despite the fact that a corresponding word object108 was identified only as a result of a spelling substitution. Thespelling substitution aspect of the disambiguation routine 22 thusadvantageously operates in a fashion transparent to the user.

A result opposite that described above would be obtained if the memory20 had stored therein a word object 108 for “dass” but not for “daβ”.For instance, the user entering an exemplary input such as thekeystrokes <DF> <AS> <β> would have as a proposed output “daβ” eventhough the memory had stored therein a word object 108 only for “dass”.It is noted that any spelling substitution particular to any languageactive on the handheld electronic device 4 can be employed.

While specific embodiments of the disclosed and claimed concept havebeen described in detail, it will be appreciated by those skilled in theart that various modifications and alternatives to those details couldbe developed in light of the overall teachings of the disclosure.Accordingly, the particular arrangements disclosed are meant to beillustrative only and not limiting as to the scope of the disclosed andclaimed concept which is to be given the full breadth of the claimsappended and any and all equivalents thereof.

1. A method of enabling input into a handheld electronic device of atype including an input apparatus, an output apparatus, and a memoryhaving stored therein a plurality of objects including a plurality oflanguage objects and a plurality of frequency objects, at least some ofthe language objects each being associated with an associated frequencyobject, the plurality of language objects including a plurality of wordobjects and a plurality of N-gram objects, each N-gram object includingat least a first linguistic element, the input apparatus including aplurality of input members, at least some of the input members eachhaving a plurality of linguistic elements assigned thereto, the methodcomprising: detecting as an ambiguous input a number of input memberactuations; generating a number of prefix objects each including anumber of the linguistic elements of the ambiguous input; for each of atleast some of the prefix objects, seeking a word object correspondingwith the prefix object; generating as a potential artificial variant anobject comprising a number of the linguistic elements of the ambiguousinput and for which a corresponding word object does not exist; making adetermination that at least one of: at least a portion of the potentialartificial variant corresponds with an N-gram object associated with afrequency object having a frequency value below a predeterminedthreshold, and for at least a portion of the potential artificialvariant, no N-gram object corresponds therewith; and performing at leastone of: outputting as an artificial variant a representation of thepotential artificial variant at a position of relatively lower prioritythan a representation of another prefix object, and suppressing from theoutput the potential artificial variant.
 2. The method of claim 1,further comprising making as at least a part of said determination adetermination that a final three linguistic elements of the potentialartificial variant correspond with an N-gram object having threelinguistic elements.
 3. The method of claim 1, further comprising makingas at least a part of said determination a determination that no N-gramobject having three linguistic elements corresponds with a final threelinguistic elements of the potential artificial variant.
 4. A handheldelectronic device comprising: an input apparatus comprising a pluralityof input members, at least some of the input members each having aplurality of linguistic elements assigned thereto; a processor apparatuscomprising a processor and a memory having stored therein a plurality ofobjects comprising a plurality of language objects and a plurality offrequency objects, at least some of the language objects each beingassociated with an associated frequency object, the plurality oflanguage objects comprising a plurality of word objects and a pluralityof N-gram objects, each N-gram object comprising at least a firstlinguistic element, the processor apparatus being structured to detectas an ambiguous input a number of input member actuations, and beingfurther structured to generate a number of prefix objects each includinga number of the linguistic elements of the ambiguous input; an outputapparatus; for each of at least some of the prefix objects, theprocessor apparatus being structured to seek a word object correspondingwith the prefix object and, for at least a number of said prefixobjects, to fail to identify a word object corresponding therewith; theprocessor apparatus being structured to identify at least one prefixobject of said number of prefix objects as being a potential artificialvariant and to make a determination that at least one of: at least aportion of the potential artificial variant corresponds with an N-gramobject associated with a frequency object having a frequency value belowa predetermined threshold, and for at least a portion of the potentialartificial variant, no N-gram object corresponds therewith; and theprocessor apparatus being structured to at least one of: output to theoutput apparatus as an artificial variant a representation of thepotential artificial variant at a position of relatively lower prioritythan a representation of another prefix object, and suppressing from anoutput to the output apparatus the potential artificial variant.
 5. Thehandheld electronic device of claim 4 wherein the processor apparatus isstructured to make as at least a part of said determination adetermination that a final three linguistic elements of the potentialartificial variant correspond with an N-gram object having threelinguistic elements.
 6. The handheld electronic device of claim 4wherein the processor apparatus is structured to make as at least a partof said determination a determination that no N-gram object having threelinguistic elements corresponds with a final three linguistic elementsof the potential artificial variant.